A Cerebellar Model of Timing and Prediction
in the Control of Reaching

Andrew G. Barto,
Andrew H. Fagg,
Nathan Sitkoff,
and James C. Houk*

Department of Computer Science, University of Massachusetts*Department of Physiology, Northwestern University Medical School

Abstract

A simplified model of the cerebellum was developed to explore its
potential for adaptive, predictive control based on delayed
feedback information. An abstract representation of a single
Purkinje cell with multistable properties was interfaced, via a
formalized premotor network, with a simulated single
degree-of-freedom limb. The limb actuator was a nonlinear
spring-mass system based on the nonlinear velocity dependence of
the stretch reflex. By including realistic mossy fiber signals, as
well as realistic conduction delays in afferent and efferent
pathways, the model allowed the investigation of timing and
predictive processes relevant to cerebellar involvement in the
control of movement. The model regulates movement by learning to
react in an anticipatory fashion to sensory feedback. Learning
depends on training information generated from corrective movements
and uses a temporally-asymmetric form of plasticity for the
parallel fiber synapses on Purkinje cells.

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